Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat/GLAS and Landsat/TM Data

Hong Chi,Wenjian Ni,Rendong Li,Xianyou Ren,Guoqing Sun,Jinliang Huang,Anmin Fu

doi:10.3390/rs9070707

Abstract

Mapping the magnitude and spatial distribution of forest aboveground biomass (AGB, in Mg·ha−1) is crucial to improve our understanding of the terrestrial carbon cycle. Landsat/TM (Thematic Mapper) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System) data were integrated to estimate the AGB in the Changbai Mountain area. Firstly, four forest types were delineated according to TM data classification. Secondly, different models for prediction of the AGB at the GLAS footprint level were developed from GLAS waveform metrics and the AGB was derived from field observations using multiple stepwise regression. Lastly, GLAS-derived AGB, in combination with vegetation indices, leaf area index (LAI), canopy closure, and digital elevation model (DEM), were used to drive a data fusion model based on the random forest approach for extrapolating the GLAS footprint AGB to a continuous AGB map. The classification result showed that the Changbai Mountain region was characterized as forest-rich in altitudinal vegetation zones. The contribution of remote sensing variables in modeling the AGB was evaluated. Vegetation index metrics account for large amount of contribution in AGB ranges <150 Mg·ha−1, while canopy closure has the largest contribution in AGB ranges ≥150 Mg·ha−1. Our study revealed that spatial information from two sensors and DEM could be combined to estimate the AGB with an R2 of 0.72 and an RMSE of 25.24 Mg·ha−1 in validation at stand level (size varied from ~0.3 ha to ~3 ha).

Highlights

Forest play an important role in the global carbon cycle [1]
In order to evaluate the contribution of remote sensing variables in modeling the AGB, we explored the variable importance of all prediction metrics using
Based on our results and those of researchers listed above, we suggest that Geoscience Laser Altimeter System (GLAS) variables closely related to mean canopy height or total waveform length, quartile and decile heights (h75 and h100) be included in the list of candidate predictors in GLAS variable selection performance aimed at estimating AGB

Summary

Introduction

Forest play an important role in the global carbon cycle [1]. Deforestation and forest degradation impact the estimation of anthropogenic greenhouse gas emissions and carbon stock in forests [2,3].Forest carbon stocks that change dynamically over time can be monitored through regular mapping of the total forest aboveground biomass (AGB, in Mg·ha−1 ) [3,4,5]. Forest play an important role in the global carbon cycle [1]. Deforestation and forest degradation impact the estimation of anthropogenic greenhouse gas emissions and carbon stock in forests [2,3]. Forest carbon stocks that change dynamically over time can be monitored through regular mapping of the total forest aboveground biomass (AGB, in Mg·ha−1 ) [3,4,5]. Mapping the magnitude and spatial distribution of forest AGB is necessary for improving estimates of terrestrial carbon sources and sinks. The traditional methods to estimate forest AGB is based on field measurements or long-term. 2017, 9, 707 forest inventories; these methods can obtain good AGB estimation.

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